Long-term Development of Time Series in Foreign Trade of the Czech Republic According to SITC Categories

This contribution presents results obtained by means of a time series analysis of import, export, turnover, balance of trade, coverage indicator of export with import, according to SITC classes (3rd Rev.) in the Czech Republic for time interval 1993-2001. Along with description of dynamics of examined effects and their trends there following parameters were also classified: mean level, variability and percentage share of particular classification classes of foreign trade indicators.

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Time series analysis of the poverty rate for low-educated people in the Czech Republic, Slovakia and Poland over the period 2005 – 2017

Proceedings of the 22nd International Scientific Conference on Applications of Mathematics and Statistics in Economics (AMSE 2019) ◽

10.2991/amse-19.2019.13 ◽

2019 ◽

Author(s):

Dagmar Blatná

Keyword(s):

Time Series ◽

Czech Republic ◽

Time Series Analysis ◽

Poverty Rate ◽

The Czech Republic ◽

Series Analysis ◽

Low Educated

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Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽

10.3390/electronics10101151 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1151

Author(s):

Carolina Gijón ◽

Matías Toril ◽

Salvador Luna-Ramírez ◽

María Luisa Marí-Altozano ◽

José María Ruiz-Avilés

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Analysis ◽

Data Traffic ◽

Medium Term ◽

Short Term ◽

Traffic Forecasting ◽

Series Analysis ◽

Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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